This website uses cookies to deliver some of our products and services as well as for analytics and to provide you a more personalized experience. Click here to learn more. By continuing to use this site, you agree to our use of cookies. We’ve also updated our Privacy Notice. Click here to see what’s new.

About Optics & Photonics TopicsOSA Publishing developed the Optics and Photonics Topics to help organize its diverse content more accurately by topic area. This topic browser contains over 2400 terms and is organized in a three-level hierarchy. Read more.

Topics can be refined further in the search results. The Topic facet will reveal the high-level topics associated with the articles returned in the search results.

Abstract

We present two approaches to speckle tracking for optical coherence tomography (OCT)-based elastography, one appropriate for small speckle motions and the other for large, rapid speckle motions. Both approaches have certain advantages over traditional cross-correlation based motion algorithms. We apply our algorithms to quantifying the strain response of a mechanically inhomogeneous, bilayered polyvinyl alcohol tissue phantom that is subjected to either small or large dynamic compressive forces while being imaged with a spectral domain OCT system. In both the small and large deformation scenarios, the algorithms performed well, clearly identifying the two mechanically disparate regions of the phantom. The stiffness ratio between the two regions was estimated to be the same for the two scenarios and both estimates agreed with the expected stiffness ratio based on earlier mechanical testing. No single numerical approach is appropriate for all cases and the experimental conditions dictate the proper choice of speckle shift algorithm for OCT-based elastography studies.

Fig. 3. Neighborhood operation on the elastogram of cumulative strain. Figure 3(a). (left) is the elastogram of the total cumulative strain as determined by the pixel-by-pixel speckle motion estimator. Figure 3(b). (center) is an enlarged picture of the 40×40 pixel convolution kernel used in the neighborhood operation. The kernel was taken directly from gray-scale values within the small box outlined in Figure 3(a). Figure 3(c). (right) is the final featurebased elastogram encoded to display the relative cumulative strains in the different layers of the tissue phantom. The highest strain in the less-stiff region was normalized to unity. The mechanical distinction between the two layers is evident and the interfacial region is quite visible (greenish-blue).

Fig. 6. (a). Displacement and (b). strain profiles plotted against time compared with the synchronized separate measurements of actual displacement [top curve in (a)] and force [bottom curve in (b)] applied to the phantom, respectively. Rest of curves from bottom to top are the depth profiles at z=0.29 mm (blue) and 0.55 mm (red), respectively.